Abstract
Numerous doubly classified models were discussed in the various chapters. Do these models sufficient for fitting all kinds of doubly classified data? The answer is probably no. So, what if they do not? Could we create our own doubly classified model? The answer is obviously a yes. Data analyzers and researchers probably know the data better after preliminary analyzes. The question is how to proceed to create a new model to fit the data. This chapter lays out a few basic principles for modeling doubly classified table models and introduces several new doubly classified models using these principles and shows how easily this could be carried out.
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Tan, T. K., & Sheng, Y. Z. (2015). Extending the quasi-symmetry model: Quasi-symmetry with n degree. Poster presented at the useR! Conference 2015
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Tan, T.K. (2017). Creating Doubly Classified Models. In: Doubly Classified Model with R. Springer, Singapore. https://doi.org/10.1007/978-981-10-6995-6_9
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DOI: https://doi.org/10.1007/978-981-10-6995-6_9
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